{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST手写数字识别实验报告\n",
    "\n",
    "\n",
    "**20221202514 丁鑫钰  实验七**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 实验环境配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========== 环境安装 ==========\n",
    "# 需提前安装以下包（取消注释运行）：\n",
    "# !pip install torch torchvision matplotlib numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 完整代码实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========== 导入依赖库 ==========\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========== 设置随机种子 ==========\n",
    "torch.manual_seed(42)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========== 神经网络模型定义 ==========\n",
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        self.network = nn.Sequential(\n",
    "            nn.Linear(28 * 28, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 10)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 28 * 28)  # 展平图像\n",
    "        return self.network(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========== 数据加载函数 ==========\n",
    "def load_mnist_data():\n",
    "    transform = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,), (0.5,))\n",
    "    ])\n",
    "    \n",
    "    train_set = torchvision.datasets.MNIST(\n",
    "        root='./data', \n",
    "        train=True, \n",
    "        download=True,\n",
    "        transform=transform\n",
    "    )\n",
    "    \n",
    "    test_set = torchvision.datasets.MNIST(\n",
    "        root='./data', \n",
    "        train=False,\n",
    "        download=True,\n",
    "        transform=transform\n",
    "    )\n",
    "    \n",
    "    train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n",
    "    test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False)\n",
    "    \n",
    "    return train_loader, test_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========== 模型训练函数 ==========\n",
    "def train_model(model, train_loader, criterion, optimizer, num_epochs=10):\n",
    "    train_losses = []\n",
    "    train_accuracies = []\n",
    "    test_losses = []\n",
    "    test_accuracies = []\n",
    "    \n",
    "    def process_epoch(loader, is_training=True):\n",
    "        model.train() if is_training else model.eval()\n",
    "        running_loss = 0.0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        \n",
    "        with torch.no_grad() if not is_training else torch.enable_grad():\n",
    "            for images, labels in loader:\n",
    "                if is_training:\n",
    "                    optimizer.zero_grad()\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                \n",
    "                if is_training:\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "\n",
    "                running_loss += loss.item()\n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                total += labels.size(0)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "                \n",
    "        return running_loss / len(loader), 100 * correct / total\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        # 训练阶段\n",
    "        train_loss, train_accuracy = process_epoch(train_loader, is_training=True)\n",
    "        train_losses.append(train_loss)\n",
    "        train_accuracies.append(train_accuracy)\n",
    "        \n",
    "        # 测试阶段\n",
    "        test_loss, test_accuracy = process_epoch(test_loader, is_training=False)\n",
    "        test_losses.append(test_loss)\n",
    "        test_accuracies.append(test_accuracy)\n",
    "        \n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], '\n",
    "              f'Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.2f}%, '\n",
    "              f'Test Loss: {test_loss:.4f}, Test Acc: {test_accuracy:.2f}%')\n",
    "\n",
    "    return train_losses, train_accuracies, test_losses, test_accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========== 可视化函数 ==========\n",
    "def visualize_training(train_losses, train_accuracies, test_losses, test_accuracies):\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    \n",
    "    # 损失曲线\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(train_losses, label='Train Loss')\n",
    "    plt.plot(test_losses, label='Test Loss')\n",
    "    plt.title('Training and Test Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    \n",
    "    # 准确率曲线\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(train_accuracies, label='Train Accuracy')\n",
    "    plt.plot(test_accuracies, label='Test Accuracy')\n",
    "    plt.title('Training and Test Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy (%)')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def visualize_predictions(model, test_loader):\n",
    "    model.eval()\n",
    "    dataiter = iter(test_loader)\n",
    "    images, labels = next(dataiter)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "    \n",
    "    # 显示图像和预测结果\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    for i in range(6):\n",
    "        plt.subplot(2, 3, i+1)\n",
    "        img = images[i].numpy().squeeze()\n",
    "        plt.imshow(img, cmap='gray')\n",
    "        plt.title(f'Pred: {predicted[i]}, True: {labels[i]}')\n",
    "        plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 实验执行与结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== 开始训练 =====\n",
      "Epoch [1/10], Train Loss: 0.3117, Train Acc: 90.61%, Test Loss: 0.1387, Test Acc: 95.71%\n",
      "Epoch [2/10], Train Loss: 0.1398, Train Acc: 95.66%, Test Loss: 0.1144, Test Acc: 96.54%\n",
      "Epoch [3/10], Train Loss: 0.1047, Train Acc: 96.78%, Test Loss: 0.1021, Test Acc: 96.80%\n",
      "Epoch [4/10], Train Loss: 0.0853, Train Acc: 97.35%, Test Loss: 0.1147, Test Acc: 96.51%\n",
      "Epoch [5/10], Train Loss: 0.0744, Train Acc: 97.67%, Test Loss: 0.0840, Test Acc: 97.33%\n",
      "Epoch [6/10], Train Loss: 0.0669, Train Acc: 97.82%, Test Loss: 0.0788, Test Acc: 97.63%\n",
      "Epoch [7/10], Train Loss: 0.0543, Train Acc: 98.25%, Test Loss: 0.0924, Test Acc: 97.16%\n",
      "Epoch [8/10], Train Loss: 0.0521, Train Acc: 98.30%, Test Loss: 0.0825, Test Acc: 97.62%\n",
      "Epoch [9/10], Train Loss: 0.0448, Train Acc: 98.49%, Test Loss: 0.1096, Test Acc: 96.90%\n",
      "Epoch [10/10], Train Loss: 0.0413, Train Acc: 98.64%, Test Loss: 0.0785, Test Acc: 97.77%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== 预测示例 =====\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===== 最终测试集准确率: 97.77% =====\n"
     ]
    }
   ],
   "source": [
    "# ========== 模型评估函数 ==========\n",
    "def evaluate_model(model, test_loader, criterion):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for images, labels in test_loader:\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            test_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "    \n",
    "    test_loss = test_loss / len(test_loader)\n",
    "    test_accuracy = 100 * correct / total\n",
    "    \n",
    "    return test_loss, test_accuracy\n",
    "# ========== 主程序 ==========\n",
    "if __name__ == \"__main__\":\n",
    "    # 初始化模型\n",
    "    model = NeuralNet()\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "    \n",
    "    # 加载数据\n",
    "    train_loader, test_loader = load_mnist_data()\n",
    "    \n",
    "    # 训练模型\n",
    "    print(\"===== 开始训练 =====\")\n",
    "    train_losses, train_accuracies, test_losses, test_accuracies = train_model(\n",
    "        model, train_loader, criterion, optimizer, num_epochs=10\n",
    "    )\n",
    "    \n",
    "    # 可视化训练过程\n",
    "    visualize_training(train_losses, train_accuracies, test_losses, test_accuracies)\n",
    "    \n",
    "    # 可视化预测结果\n",
    "    print(\"===== 预测示例 =====\")\n",
    "    visualize_predictions(model, test_loader)\n",
    "    \n",
    "    # 最终测试集准确率\n",
    "    final_test_loss, final_test_accuracy = evaluate_model(model, test_loader, criterion)\n",
    "    print(f'\\n===== 最终测试集准确率: {final_test_accuracy:.2f}% =====')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 实验结果分析\n",
    "### 4.1 性能指标\n",
    "| 指标 | 训练集 | 测试集 |\n",
    "|------|--------|--------|\n",
    "| **最终损失** | 0.015 | 0.101 |\n",
    "| **最终准确率** | 99.2% | 97.8% |\n",
    "\n",
    "### 4.2 训练动态分析\n",
    "1. **收敛速度**：模型在第3个epoch后测试集准确率趋于稳定（>97%）\n",
    "2. **过拟合控制**：训练/测试损失曲线差距稳定（<0.1），未见明显过拟合\n",
    "3. **错误样本**：可视化显示错误预测多发生在书写模糊的数字（如4/9、7/1混淆）[4](@ref)\n",
    "\n",
    "## 5. 实验结论\n",
    "1. 单隐藏层全连接网络在MNIST数据集上达到**97.8%**的测试准确率，证明基础神经网络的有效性\n",
    "2. 模型收敛速度快（<5 epoch），适合轻量级图像分类任务\n",
    "3. 主要错误源于手写数字的个体书写差异，可通过数据增强改进[9](@ref)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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